In Fig. REF , the impact of power control on the throughput with LCMMSE is characterized.
For this plot, users transmit at powers that are dependent on their distances from the BS.
Specifically, the \(m\) th user, who is located at a distance \(r_m\)  from the BS transmits at a power \(P(r_m/r_0)^{\alpha - \zeta }\) , making \(\zeta \)  the effective path loss exponent.
The cell edge SNR is fixed to 10 dB, and the throughputs are obtained by varying \(\zeta \)  and \(P\) .
When \(\zeta = 0\) , the signals of the users undergo pure fading, and the system achieves a peak throughput of \(\mathcal {T} = 1.52\)  at \(L = 1.6\) .
Further, as \(L\)  is increased, the throughput drops to 0.
The throughput of the system increases as \(\zeta \)  increases, until \(\zeta = 2/3\) .
The exact \(\zeta \)  that yields the highest throughput is dependent on other system parameters such as SNR, \(\gamma _{\text{th}}\) , and \(N\) .
As \(\zeta \)  is increased, the channel coefficients of the users become more disparate, and thus offer a higher degree of capture effect.
Beyond \(\zeta = 3\) , the throughput decreases as the exponent is so high that the received signal power becomes comparable to the noise.
For higher \(\zeta \) , the throughput saturates as \(L\)  is increased since a few users are always decoded due to path loss disparity.
The channel fades and the path loss coefficients contribute to the disparity amongst the channel coefficients of the users, and thus such a system has higher throughputs than a system with only path loss [1]} or only fading [2]}.
Thus, it is useful to consider the combined effects of fading and path loss in optimizing the performance.
